Macro-level collision prediction using geographically weighted negative binomial regression
Bibliographic record
Abstract
We developed and tested geographically weighted Poisson regression and geographically weighted negative binomial regression models using five year’s collisions, traffic, socio-demographic, road inventory, and land use data for Regina, Saskatchewan, Canada. The need for geographically weighted models became clear when Moran’s I local indicator showed that our study data contained statistically significant levels of spatial autocorrelation. Bandwidth is a required input for geographically weighted regression models. We tested fixed and adaptive bandwidths. We found that fixed bandwidth was more suitable than adaptive bandwidth in our study. Models that used fixed and adaptive bandwidth produced a wide range of parameters across zones. We think the wide range of parameters helped explain unobserved heterogeneity issues within the zones. To compare the geographically weighted Poisson and geographically weighted negative binomial models, we applied seven well-known goodness-of-fit tests. The results were inconsistent, but the cumulative residual plot developed for each model showed that the fixed bandwidth geographically weighted Poisson model and the geographically weighted negative binomial model were better at predicting collisions than were the adaptive bandwidth models. Based on the CURE plots obtained, we concluded that the geographically weighted negative binomial model with fixed bandwidth was the best model for our study data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".